Overview

Dataset statistics

Number of variables33
Number of observations172
Missing cells1572
Missing cells (%)27.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.5 KiB
Average record size in memory264.7 B

Variable types

Numeric9
Categorical21
Unsupported3

Warnings

DESCRIPCION-ENTIDAD has constant value "Residencia de personas mayores dependientes." Constant
PUERTA has constant value "A" Constant
LOCALIDAD has constant value "MADRID" Constant
PROVINCIA has constant value "MADRID" Constant
Unnamed: 32 has constant value " " Constant
NOMBRE has a high cardinality: 172 distinct values High cardinality
TRANSPORTE has a high cardinality: 159 distinct values High cardinality
CONTENT-URL has a high cardinality: 172 distinct values High cardinality
NOMBRE-VIA has a high cardinality: 154 distinct values High cardinality
BARRIO has a high cardinality: 87 distinct values High cardinality
TELEFONO has a high cardinality: 166 distinct values High cardinality
COORDENADA-X is highly correlated with LONGITUDHigh correlation
COORDENADA-Y is highly correlated with LATITUDHigh correlation
LATITUD is highly correlated with COORDENADA-YHigh correlation
LONGITUD is highly correlated with COORDENADA-XHigh correlation
DESCRIPCION-ENTIDAD has 171 (99.4%) missing values Missing
HORARIO has 168 (97.7%) missing values Missing
EQUIPAMIENTO has 23 (13.4%) missing values Missing
TRANSPORTE has 4 (2.3%) missing values Missing
DESCRIPCION has 167 (97.1%) missing values Missing
PLANTA has 166 (96.5%) missing values Missing
PUERTA has 171 (99.4%) missing values Missing
ESCALERAS has 172 (100.0%) missing values Missing
ORIENTACION has 165 (95.9%) missing values Missing
COD-BARRIO has 6 (3.5%) missing values Missing
BARRIO has 2 (1.2%) missing values Missing
COD-DISTRITO has 6 (3.5%) missing values Missing
LATITUD has 2 (1.2%) missing values Missing
LONGITUD has 2 (1.2%) missing values Missing
TELEFONO has 2 (1.2%) missing values Missing
FAX has 172 (100.0%) missing values Missing
EMAIL has 172 (100.0%) missing values Missing
NOMBRE is uniformly distributed Uniform
TRANSPORTE is uniformly distributed Uniform
CONTENT-URL is uniformly distributed Uniform
NOMBRE-VIA is uniformly distributed Uniform
ORIENTACION is uniformly distributed Uniform
TELEFONO is uniformly distributed Uniform
PK has unique values Unique
NOMBRE has unique values Unique
CONTENT-URL has unique values Unique
ESCALERAS is an unsupported type, check if it needs cleaning or further analysis Unsupported
FAX is an unsupported type, check if it needs cleaning or further analysis Unsupported
EMAIL is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-10-08 13:06:27.397883
Analysis finished2022-10-08 13:06:37.700818
Duration10.3 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

PK
Real number (ℝ≥0)

UNIQUE

Distinct172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4330253.733
Minimum11275
Maximum11545359
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2022-10-08T15:06:37.769818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11275
5-th percentile11315.95
Q154678.25
median5115017.5
Q36891576
95-th percentile11542498.95
Maximum11545359
Range11534084
Interquartile range (IQR)6836897.75

Descriptive statistics

Standard deviation4346750.001
Coefficient of variation (CV)1.003809538
Kurtosis-1.353358418
Mean4330253.733
Median Absolute Deviation (MAD)4995886
Skewness0.3705503415
Sum744803642
Variance1.889423557 × 1013
MonotocityNot monotonic
2022-10-08T15:06:37.910819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237111
 
0.6%
1444631
 
0.6%
113111
 
0.6%
113801
 
0.6%
1316091
 
0.6%
118481
 
0.6%
59372281
 
0.6%
59350001
 
0.6%
108433101
 
0.6%
360671
 
0.6%
Other values (162)162
94.2%
ValueCountFrequency (%)
112751
0.6%
112791
0.6%
112821
0.6%
112851
0.6%
112881
0.6%
ValueCountFrequency (%)
115453591
0.6%
115452291
0.6%
115452081
0.6%
115451931
0.6%
115451821
0.6%

NOMBRE
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Apartamentos Municipales para Mayores 'Retiro'
 
1
Residencia para mayores Sanitas Residencial Puerta de Hierro
 
1
Residencia para mayores San Enrique y Santa Rita
 
1
Residencia para mayores San Francisco de Paula
 
1
Residencia para mayores San José
 
1
Other values (167)
167 

Length

Max length91
Median length45
Mean length46.78488372
Min length22

Characters and Unicode

Total characters8047
Distinct characters63
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)100.0%

Sample

1st rowApartamentos Municipales para Mayores 'Retiro'
2nd rowApartamentos Municipales para Mayores 'San Francisco'
3rd rowCentro de día Gerontológico de Estancia Diurna
4th rowCentro de día Vivendia Personas Mayores
5th rowCentro de día para enfermos de alzheimer Reina Sofía (calle Infanta Mercedes 26)
ValueCountFrequency (%)
Apartamentos Municipales para Mayores 'Retiro'1
 
0.6%
Residencia para mayores Sanitas Residencial Puerta de Hierro1
 
0.6%
Residencia para mayores San Enrique y Santa Rita1
 
0.6%
Residencia para mayores San Francisco de Paula1
 
0.6%
Residencia para mayores San José1
 
0.6%
Residencia para mayores San Juan de Dios1
 
0.6%
Residencia para mayores San Luis Gonzaga (Los Almendros)1
 
0.6%
Residencia para mayores Sanitas El Viso1
 
0.6%
Residencia para mayores Sanitas Residencial Alameda1
 
0.6%
Residencia para mayores Sanitas Residencial La Florida1
 
0.6%
Other values (162)162
94.2%
2022-10-08T15:06:38.201540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mayores167
 
14.2%
para163
 
13.9%
residencia156
 
13.3%
de77
 
6.6%
centro55
 
4.7%
día50
 
4.3%
y42
 
3.6%
santa11
 
0.9%
san11
 
0.9%
los11
 
0.9%
Other values (255)429
36.6%

Most occurring characters

ValueCountFrequency (%)
a1191
14.8%
1001
12.4%
e866
10.8%
r632
 
7.9%
i528
 
6.6%
s501
 
6.2%
o401
 
5.0%
d377
 
4.7%
n374
 
4.6%
c251
 
3.1%
Other values (53)1925
23.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6394
79.5%
Space Separator1001
 
12.4%
Uppercase Letter622
 
7.7%
Other Punctuation11
 
0.1%
Open Punctuation6
 
0.1%
Close Punctuation6
 
0.1%
Decimal Number4
 
< 0.1%
Dash Punctuation3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a1191
18.6%
e866
13.5%
r632
9.9%
i528
8.3%
s501
7.8%
o401
 
6.3%
d377
 
5.9%
n374
 
5.8%
c251
 
3.9%
y212
 
3.3%
Other values (20)1061
16.6%
ValueCountFrequency (%)
R171
27.5%
S60
 
9.6%
C59
 
9.5%
M50
 
8.0%
A40
 
6.4%
V30
 
4.8%
L29
 
4.7%
P26
 
4.2%
E20
 
3.2%
I17
 
2.7%
Other values (13)120
19.3%
ValueCountFrequency (%)
21
25.0%
61
25.0%
71
25.0%
51
25.0%
ValueCountFrequency (%)
'10
90.9%
,1
 
9.1%
ValueCountFrequency (%)
1001
100.0%
ValueCountFrequency (%)
(6
100.0%
ValueCountFrequency (%)
)6
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7016
87.2%
Common1031
 
12.8%

Most frequent character per script

ValueCountFrequency (%)
a1191
17.0%
e866
12.3%
r632
 
9.0%
i528
 
7.5%
s501
 
7.1%
o401
 
5.7%
d377
 
5.4%
n374
 
5.3%
c251
 
3.6%
y212
 
3.0%
Other values (43)1683
24.0%
ValueCountFrequency (%)
1001
97.1%
'10
 
1.0%
(6
 
0.6%
)6
 
0.6%
-3
 
0.3%
21
 
0.1%
61
 
0.1%
71
 
0.1%
51
 
0.1%
,1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7949
98.8%
Latin 1 Sup98
 
1.2%

Most frequent character per block

ValueCountFrequency (%)
a1191
15.0%
1001
12.6%
e866
10.9%
r632
 
8.0%
i528
 
6.6%
s501
 
6.3%
o401
 
5.0%
d377
 
4.7%
n374
 
4.7%
c251
 
3.2%
Other values (46)1827
23.0%
ValueCountFrequency (%)
í70
71.4%
ñ9
 
9.2%
á7
 
7.1%
ó6
 
6.1%
é4
 
4.1%
Á1
 
1.0%
ú1
 
1.0%

DESCRIPCION-ENTIDAD
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing171
Missing (%)99.4%
Memory size1.5 KiB
Residencia de personas mayores dependientes.

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters44
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowResidencia de personas mayores dependientes.
ValueCountFrequency (%)
Residencia de personas mayores dependientes.1
 
0.6%
(Missing)171
99.4%
2022-10-08T15:06:38.399749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:38.454766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
residencia1
20.0%
de1
20.0%
personas1
20.0%
mayores1
20.0%
dependientes1
20.0%

Most occurring characters

ValueCountFrequency (%)
e9
20.5%
s5
11.4%
d4
9.1%
n4
9.1%
4
9.1%
i3
 
6.8%
a3
 
6.8%
p2
 
4.5%
r2
 
4.5%
o2
 
4.5%
Other values (6)6
13.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter38
86.4%
Space Separator4
 
9.1%
Uppercase Letter1
 
2.3%
Other Punctuation1
 
2.3%

Most frequent character per category

ValueCountFrequency (%)
e9
23.7%
s5
13.2%
d4
10.5%
n4
10.5%
i3
 
7.9%
a3
 
7.9%
p2
 
5.3%
r2
 
5.3%
o2
 
5.3%
c1
 
2.6%
Other values (3)3
 
7.9%
ValueCountFrequency (%)
R1
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39
88.6%
Common5
 
11.4%

Most frequent character per script

ValueCountFrequency (%)
e9
23.1%
s5
12.8%
d4
10.3%
n4
10.3%
i3
 
7.7%
a3
 
7.7%
p2
 
5.1%
r2
 
5.1%
o2
 
5.1%
R1
 
2.6%
Other values (4)4
10.3%
ValueCountFrequency (%)
4
80.0%
.1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII44
100.0%

Most frequent character per block

ValueCountFrequency (%)
e9
20.5%
s5
11.4%
d4
9.1%
n4
9.1%
4
9.1%
i3
 
6.8%
a3
 
6.8%
p2
 
4.5%
r2
 
4.5%
o2
 
4.5%
Other values (6)6
13.6%

HORARIO
Categorical

MISSING

Distinct3
Distinct (%)75.0%
Missing168
Missing (%)97.7%
Memory size1.5 KiB
De lunes a domingo de 9 a 22 horas.
De lunes a viernes de 9 a 18 horas.
Residencia de personas mayores dependientes y centro de día.

Length

Max length60
Median length35
Mean length41.25
Min length35

Characters and Unicode

Total characters165
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowDe lunes a domingo de 9 a 22 horas.
2nd rowDe lunes a domingo de 9 a 22 horas.
3rd rowDe lunes a viernes de 9 a 18 horas.
4th rowResidencia de personas mayores dependientes y centro de día.
ValueCountFrequency (%)
De lunes a domingo de 9 a 22 horas.2
 
1.2%
De lunes a viernes de 9 a 18 horas.1
 
0.6%
Residencia de personas mayores dependientes y centro de día.1
 
0.6%
(Missing)168
97.7%
2022-10-08T15:06:38.614732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:38.675731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
de8
22.2%
a6
16.7%
lunes3
 
8.3%
93
 
8.3%
horas3
 
8.3%
domingo2
 
5.6%
222
 
5.6%
viernes1
 
2.8%
181
 
2.8%
residencia1
 
2.8%
Other values (6)6
16.7%

Most occurring characters

ValueCountFrequency (%)
32
19.4%
e22
13.3%
a13
 
7.9%
s12
 
7.3%
n11
 
6.7%
d11
 
6.7%
o10
 
6.1%
r7
 
4.2%
i6
 
3.6%
24
 
2.4%
Other values (17)37
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter116
70.3%
Space Separator32
 
19.4%
Decimal Number9
 
5.5%
Uppercase Letter4
 
2.4%
Other Punctuation4
 
2.4%

Most frequent character per category

ValueCountFrequency (%)
e22
19.0%
a13
11.2%
s12
10.3%
n11
9.5%
d11
9.5%
o10
8.6%
r7
 
6.0%
i6
 
5.2%
l3
 
2.6%
u3
 
2.6%
Other values (9)18
15.5%
ValueCountFrequency (%)
24
44.4%
93
33.3%
11
 
11.1%
81
 
11.1%
ValueCountFrequency (%)
D3
75.0%
R1
 
25.0%
ValueCountFrequency (%)
32
100.0%
ValueCountFrequency (%)
.4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin120
72.7%
Common45
 
27.3%

Most frequent character per script

ValueCountFrequency (%)
e22
18.3%
a13
10.8%
s12
10.0%
n11
9.2%
d11
9.2%
o10
8.3%
r7
 
5.8%
i6
 
5.0%
D3
 
2.5%
l3
 
2.5%
Other values (11)22
18.3%
ValueCountFrequency (%)
32
71.1%
24
 
8.9%
.4
 
8.9%
93
 
6.7%
11
 
2.2%
81
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII164
99.4%
Latin 1 Sup1
 
0.6%

Most frequent character per block

ValueCountFrequency (%)
32
19.5%
e22
13.4%
a13
 
7.9%
s12
 
7.3%
n11
 
6.7%
d11
 
6.7%
o10
 
6.1%
r7
 
4.3%
i6
 
3.7%
24
 
2.4%
Other values (16)36
22.0%
ValueCountFrequency (%)
í1
100.0%

EQUIPAMIENTO
Categorical

MISSING

Distinct17
Distinct (%)11.4%
Missing23
Missing (%)13.4%
Memory size1.5 KiB
Residencia de personas mayores dependientes.
65 
Residencia de personas mayores mixta.
41 
Residencia de personas mayores dependientes y centro de día.
19 
Residencia de personas mayores mixta y centro de día.
 
5
Centro de día y residencia de personas mayores mixta.
 
3
Other values (12)
16 

Length

Max length150
Median length44
Mean length46.51006711
Min length12

Characters and Unicode

Total characters6930
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)5.4%

Sample

1st rowAlojamiento.
2nd rowAlojamiento.
3rd rowCentro de día y residencia de personas mayores mixta.
4th rowCentro de día y residencia de personas mayores dependientes.
5th rowCentro de día y residencia de personas mayores mixta.
ValueCountFrequency (%)
Residencia de personas mayores dependientes.65
37.8%
Residencia de personas mayores mixta.41
23.8%
Residencia de personas mayores dependientes y centro de día.19
 
11.0%
Residencia de personas mayores mixta y centro de día.5
 
2.9%
Centro de día y residencia de personas mayores mixta.3
 
1.7%
Alojamiento.2
 
1.2%
Centro de día y residencia de personas mayores dependientes.2
 
1.2%
Asistencia médica y enfermería - Fisioterapia - Rehabilitación - Terapia ocupacional - Gerocultoras 24 horas. Comedor - Gimnasio - Terraza ajardinada.2
 
1.2%
Residencia de personas mayores autónomas.2
 
1.2%
Residencia para personas mayores dependientes y centro de día.1
 
0.6%
Other values (7)7
 
4.1%
(Missing)23
 
13.4%
2022-10-08T15:06:38.899735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de175
19.2%
personas145
15.9%
mayores145
15.9%
residencia144
15.8%
dependientes89
9.8%
mixta50
 
5.5%
y36
 
4.0%
día33
 
3.6%
centro33
 
3.6%
12
 
1.3%
Other values (29)48
 
5.3%

Most occurring characters

ValueCountFrequency (%)
e1174
16.9%
761
11.0%
s690
10.0%
a580
8.4%
d541
7.8%
n524
7.6%
i465
 
6.7%
o356
 
5.1%
r355
 
5.1%
p244
 
3.5%
Other values (27)1240
17.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5838
84.2%
Space Separator761
 
11.0%
Uppercase Letter164
 
2.4%
Other Punctuation151
 
2.2%
Dash Punctuation12
 
0.2%
Decimal Number4
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e1174
20.1%
s690
11.8%
a580
9.9%
d541
9.3%
n524
9.0%
i465
 
8.0%
o356
 
6.1%
r355
 
6.1%
p244
 
4.2%
m212
 
3.6%
Other values (15)697
11.9%
ValueCountFrequency (%)
R141
86.0%
C7
 
4.3%
A6
 
3.7%
T4
 
2.4%
G4
 
2.4%
F2
 
1.2%
ValueCountFrequency (%)
.149
98.7%
,2
 
1.3%
ValueCountFrequency (%)
22
50.0%
42
50.0%
ValueCountFrequency (%)
761
100.0%
ValueCountFrequency (%)
-12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6002
86.6%
Common928
 
13.4%

Most frequent character per script

ValueCountFrequency (%)
e1174
19.6%
s690
11.5%
a580
9.7%
d541
9.0%
n524
8.7%
i465
 
7.7%
o356
 
5.9%
r355
 
5.9%
p244
 
4.1%
m212
 
3.5%
Other values (21)861
14.3%
ValueCountFrequency (%)
761
82.0%
.149
 
16.1%
-12
 
1.3%
22
 
0.2%
42
 
0.2%
,2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII6886
99.4%
Latin 1 Sup44
 
0.6%

Most frequent character per block

ValueCountFrequency (%)
e1174
17.0%
761
11.1%
s690
10.0%
a580
8.4%
d541
7.9%
n524
7.6%
i465
 
6.8%
o356
 
5.2%
r355
 
5.2%
p244
 
3.5%
Other values (24)1196
17.4%
ValueCountFrequency (%)
í35
79.5%
ó7
 
15.9%
é2
 
4.5%

TRANSPORTE
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct159
Distinct (%)94.6%
Missing4
Missing (%)2.3%
Memory size1.5 KiB
Bus: 162.
 
4
Bus: 58, 63, 145. Cercanías Renfe: Santa Eugenia (líneas C2 y C7).
 
2
Metro: Arturo Soria (línea 4). Bus: 11, 122, 70.
 
2
Metro: Pío XII (línea 9). Bus: 14, 150.
 
2
Metro: Oporto (líneas 5 y 6). Bus: 34, 35, 118.
 
2
Other values (154)
156 

Length

Max length302
Median length55
Mean length78.41666667
Min length8

Characters and Unicode

Total characters13174
Distinct characters75
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)90.5%

Sample

1st rowMetro: Sainz de BarandaBus: 15, 30, 56, 143, 202
2nd rowMetro: LatinaBus: 3, 148
3rd rowMetro: San Francisco (línea 11). Bus: 118, 47.
4th rowMetro: Usera (línea 6). Bus: 78.
5th rowMetro: Tetuán (línea 1). Bus: 126, 3. Bicimad: Estaciones 155 (calle San Germán, 57), 154 (calle Orense, 36), 152 (avenida del General Perón, 4). Aparcamiento: Presidente Carmona (33), avenida Presidente Carmona.
ValueCountFrequency (%)
Bus: 162.4
 
2.3%
Bus: 58, 63, 145. Cercanías Renfe: Santa Eugenia (líneas C2 y C7).2
 
1.2%
Metro: Arturo Soria (línea 4). Bus: 11, 122, 70.2
 
1.2%
Metro: Pío XII (línea 9). Bus: 14, 150.2
 
1.2%
Metro: Oporto (líneas 5 y 6). Bus: 34, 35, 118.2
 
1.2%
Bus: 163.2
 
1.2%
Metro: Esperanza (línea 4). Bus: 120, 122.2
 
1.2%
Bus: 116, 78. Cercanías Renfe: Orcasitas (línea C5).1
 
0.6%
Metro: Concha Espina (línea 9), Colombia (líneas 8 y 9). Bus: 51, 7, 120, 43, 14. Bicimad: Estación 159: Paseo de la Habana, 63.1
 
0.6%
Metro: Concha Espina (línea 9). Bus: 51, 7, 16, 29, 52, 19. Bicimad: Estaciones 147 (avenida del Doctor Arce, 45), 197 (avenida de Concha Espina, 34).1
 
0.6%
Other values (149)149
86.6%
(Missing)4
 
2.3%
2022-10-08T15:06:39.166365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bus165
 
7.2%
línea131
 
5.7%
metro127
 
5.5%
y65
 
2.8%
calle55
 
2.4%
de54
 
2.4%
líneas50
 
2.2%
bicimad42
 
1.8%
640
 
1.7%
440
 
1.7%
Other values (464)1520
66.4%

Most occurring characters

ValueCountFrequency (%)
2348
17.8%
a884
 
6.7%
e821
 
6.2%
,565
 
4.3%
n529
 
4.0%
l502
 
3.8%
1457
 
3.5%
s417
 
3.2%
r403
 
3.1%
o379
 
2.9%
Other values (65)5869
44.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6091
46.2%
Space Separator2348
 
17.8%
Decimal Number1848
 
14.0%
Other Punctuation1310
 
9.9%
Uppercase Letter1039
 
7.9%
Open Punctuation267
 
2.0%
Close Punctuation267
 
2.0%
Dash Punctuation4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a884
14.5%
e821
13.5%
n529
8.7%
l502
 
8.2%
s417
 
6.8%
r403
 
6.6%
o379
 
6.2%
i331
 
5.4%
t274
 
4.5%
u270
 
4.4%
Other values (20)1281
21.0%
ValueCountFrequency (%)
B246
23.7%
M154
14.8%
C152
14.6%
E67
 
6.4%
A67
 
6.4%
P56
 
5.4%
R54
 
5.2%
S41
 
3.9%
V28
 
2.7%
I26
 
2.5%
Other values (14)148
14.2%
ValueCountFrequency (%)
1457
24.7%
2219
11.9%
4206
11.1%
3205
11.1%
5173
 
9.4%
7148
 
8.0%
6142
 
7.7%
0123
 
6.7%
889
 
4.8%
986
 
4.7%
ValueCountFrequency (%)
,565
43.1%
:372
28.4%
.356
27.2%
;9
 
0.7%
'4
 
0.3%
&3
 
0.2%
/1
 
0.1%
ValueCountFrequency (%)
2348
100.0%
ValueCountFrequency (%)
(267
100.0%
ValueCountFrequency (%)
)267
100.0%
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7130
54.1%
Common6044
45.9%

Most frequent character per script

ValueCountFrequency (%)
a884
 
12.4%
e821
 
11.5%
n529
 
7.4%
l502
 
7.0%
s417
 
5.8%
r403
 
5.7%
o379
 
5.3%
i331
 
4.6%
t274
 
3.8%
u270
 
3.8%
Other values (44)2320
32.5%
ValueCountFrequency (%)
2348
38.8%
,565
 
9.3%
1457
 
7.6%
:372
 
6.2%
.356
 
5.9%
(267
 
4.4%
)267
 
4.4%
2219
 
3.6%
4206
 
3.4%
3205
 
3.4%
Other values (11)782
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII12851
97.5%
Latin 1 Sup323
 
2.5%

Most frequent character per block

ValueCountFrequency (%)
2348
18.3%
a884
 
6.9%
e821
 
6.4%
,565
 
4.4%
n529
 
4.1%
l502
 
3.9%
1457
 
3.6%
s417
 
3.2%
r403
 
3.1%
o379
 
2.9%
Other values (58)5546
43.2%
ValueCountFrequency (%)
í230
71.2%
ó39
 
12.1%
á22
 
6.8%
é12
 
3.7%
ñ9
 
2.8%
Á6
 
1.9%
ú5
 
1.5%

DESCRIPCION
Categorical

MISSING

Distinct4
Distinct (%)80.0%
Missing167
Missing (%)97.1%
Memory size1.5 KiB
Condiciones de Admisión: - Estar empadronado en el municipio de Madrid.- Ser mayor de 65 años (también el cónyuge que conviva con él, siempre que tenga cumplidos los 60 años).- Ser autónomo en la realización de las actividades básicas diarias.- Carecer de vivienda propia o ser ésta inadecuada y/o encontrarse en riesgo por vivir solo.- Estar dispuesto a ingresar en un Centro Residencial Asistido cuando no se valga por sí mismo.
Información Complementaria: Estar empadronado en el municipio de Madrid. Ser mayor de 65 años (también el cónyuge que conviva con él, siempre que tenga cumplidos los 60 años). Estar afectado de deterioro físico o relacional. Presentar una situación socio-familiar y limitación de su autonomía personal que le impida seguir viviendo en su domicilio. Plazas: 36
Información Complementaria: Estar empadronado en el Municipio de Madrid. Ser mayor de 60 años. Padecer enfermedad de Alzheimer u otra demencia. Presentar una situación socio-familiar y limitación de su autonomía personal que le impida seguir viviendo en su domicilio. Plazas: 58
Información Complementaria: Estar empadronado en el Municipio de Madrid. Ser mayor de 60 años. Padecer enfermedad de Alzheimer u otra demencia. Presentar una situación socio-familiar y limitación de su autonomía personal que le impida seguir viviendo en su domicilio. Plazas: 90.

Length

Max length430
Median length361
Mean length356.4
Min length280

Characters and Unicode

Total characters1782
Distinct characters49
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)60.0%

Sample

1st rowCondiciones de Admisión: - Estar empadronado en el municipio de Madrid.- Ser mayor de 65 años (también el cónyuge que conviva con él, siempre que tenga cumplidos los 60 años).- Ser autónomo en la realización de las actividades básicas diarias.- Carecer de vivienda propia o ser ésta inadecuada y/o encontrarse en riesgo por vivir solo.- Estar dispuesto a ingresar en un Centro Residencial Asistido cuando no se valga por sí mismo.
2nd rowCondiciones de Admisión: - Estar empadronado en el municipio de Madrid.- Ser mayor de 65 años (también el cónyuge que conviva con él, siempre que tenga cumplidos los 60 años).- Ser autónomo en la realización de las actividades básicas diarias.- Carecer de vivienda propia o ser ésta inadecuada y/o encontrarse en riesgo por vivir solo.- Estar dispuesto a ingresar en un Centro Residencial Asistido cuando no se valga por sí mismo.
3rd rowInformación Complementaria: Estar empadronado en el municipio de Madrid. Ser mayor de 65 años (también el cónyuge que conviva con él, siempre que tenga cumplidos los 60 años). Estar afectado de deterioro físico o relacional. Presentar una situación socio-familiar y limitación de su autonomía personal que le impida seguir viviendo en su domicilio. Plazas: 36
4th rowInformación Complementaria: Estar empadronado en el Municipio de Madrid. Ser mayor de 60 años. Padecer enfermedad de Alzheimer u otra demencia. Presentar una situación socio-familiar y limitación de su autonomía personal que le impida seguir viviendo en su domicilio. Plazas: 58
5th rowInformación Complementaria: Estar empadronado en el Municipio de Madrid. Ser mayor de 60 años. Padecer enfermedad de Alzheimer u otra demencia. Presentar una situación socio-familiar y limitación de su autonomía personal que le impida seguir viviendo en su domicilio. Plazas: 90.
ValueCountFrequency (%)
Condiciones de Admisión: - Estar empadronado en el municipio de Madrid.- Ser mayor de 65 años (también el cónyuge que conviva con él, siempre que tenga cumplidos los 60 años).- Ser autónomo en la realización de las actividades básicas diarias.- Carecer de vivienda propia o ser ésta inadecuada y/o encontrarse en riesgo por vivir solo.- Estar dispuesto a ingresar en un Centro Residencial Asistido cuando no se valga por sí mismo.2
 
1.2%
Información Complementaria: Estar empadronado en el municipio de Madrid. Ser mayor de 65 años (también el cónyuge que conviva con él, siempre que tenga cumplidos los 60 años). Estar afectado de deterioro físico o relacional. Presentar una situación socio-familiar y limitación de su autonomía personal que le impida seguir viviendo en su domicilio. Plazas: 361
 
0.6%
Información Complementaria: Estar empadronado en el Municipio de Madrid. Ser mayor de 60 años. Padecer enfermedad de Alzheimer u otra demencia. Presentar una situación socio-familiar y limitación de su autonomía personal que le impida seguir viviendo en su domicilio. Plazas: 581
 
0.6%
Información Complementaria: Estar empadronado en el Municipio de Madrid. Ser mayor de 60 años. Padecer enfermedad de Alzheimer u otra demencia. Presentar una situación socio-familiar y limitación de su autonomía personal que le impida seguir viviendo en su domicilio. Plazas: 90.1
 
0.6%
(Missing)167
97.1%
2022-10-08T15:06:39.414093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:39.491095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
de22
 
7.9%
en14
 
5.0%
que9
 
3.2%
ser9
 
3.2%
el8
 
2.9%
años8
 
2.9%
estar8
 
2.9%
su6
 
2.2%
empadronado5
 
1.8%
municipio5
 
1.8%
Other values (77)185
66.3%

Most occurring characters

ValueCountFrequency (%)
280
15.7%
e157
 
8.8%
a150
 
8.4%
i143
 
8.0%
o116
 
6.5%
n104
 
5.8%
r95
 
5.3%
s92
 
5.2%
d88
 
4.9%
m60
 
3.4%
Other values (39)497
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1375
77.2%
Space Separator280
 
15.7%
Uppercase Letter50
 
2.8%
Other Punctuation36
 
2.0%
Decimal Number22
 
1.2%
Dash Punctuation13
 
0.7%
Open Punctuation3
 
0.2%
Close Punctuation3
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e157
11.4%
a150
10.9%
i143
10.4%
o116
 
8.4%
n104
 
7.6%
r95
 
6.9%
s92
 
6.7%
d88
 
6.4%
m60
 
4.4%
c57
 
4.1%
Other values (17)313
22.8%
ValueCountFrequency (%)
C9
18.0%
E8
16.0%
P8
16.0%
M7
14.0%
S7
14.0%
A6
12.0%
I3
 
6.0%
R2
 
4.0%
ValueCountFrequency (%)
69
40.9%
06
27.3%
54
18.2%
31
 
4.5%
81
 
4.5%
91
 
4.5%
ValueCountFrequency (%)
.23
63.9%
:8
 
22.2%
,3
 
8.3%
/2
 
5.6%
ValueCountFrequency (%)
280
100.0%
ValueCountFrequency (%)
-13
100.0%
ValueCountFrequency (%)
(3
100.0%
ValueCountFrequency (%)
)3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1425
80.0%
Common357
 
20.0%

Most frequent character per script

ValueCountFrequency (%)
e157
11.0%
a150
10.5%
i143
 
10.0%
o116
 
8.1%
n104
 
7.3%
r95
 
6.7%
s92
 
6.5%
d88
 
6.2%
m60
 
4.2%
c57
 
4.0%
Other values (25)363
25.5%
ValueCountFrequency (%)
280
78.4%
.23
 
6.4%
-13
 
3.6%
69
 
2.5%
:8
 
2.2%
06
 
1.7%
54
 
1.1%
(3
 
0.8%
,3
 
0.8%
)3
 
0.8%
Other values (4)5
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1740
97.6%
Latin 1 Sup42
 
2.4%

Most frequent character per block

ValueCountFrequency (%)
280
16.1%
e157
 
9.0%
a150
 
8.6%
i143
 
8.2%
o116
 
6.7%
n104
 
6.0%
r95
 
5.5%
s92
 
5.3%
d88
 
5.1%
m60
 
3.4%
Other values (34)455
26.1%
ValueCountFrequency (%)
ó18
42.9%
ñ8
19.0%
é8
19.0%
í6
 
14.3%
á2
 
4.8%

ACCESIBILIDAD
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
3
165 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters172
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row3
5th row3
ValueCountFrequency (%)
3165
95.9%
17
 
4.1%
2022-10-08T15:06:39.843094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:39.901095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3165
95.9%
17
 
4.1%

Most occurring characters

ValueCountFrequency (%)
3165
95.9%
17
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number172
100.0%

Most frequent character per category

ValueCountFrequency (%)
3165
95.9%
17
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common172
100.0%

Most frequent character per script

ValueCountFrequency (%)
3165
95.9%
17
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII172
100.0%

Most frequent character per block

ValueCountFrequency (%)
3165
95.9%
17
 
4.1%

CONTENT-URL
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=ce926545f951c010VgnVCM2000000c205a0aRCRD
 
1
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=8b75ba61e681c010VgnVCM1000000b205a0aRCRD
 
1
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=b4e75a0f8161c010VgnVCM1000000b205a0aRCRD
 
1
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=4e3772081661c010VgnVCM1000000b205a0aRCRD
 
1
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=c4f06da0aa81c010VgnVCM1000000b205a0aRCRD
 
1
Other values (167)
167 

Length

Max length144
Median length144
Mean length144
Min length144

Characters and Unicode

Total characters24768
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)100.0%

Sample

1st rowhttp://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=ce926545f951c010VgnVCM2000000c205a0aRCRD
2nd rowhttp://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=8300d4985261c010VgnVCM1000000b205a0aRCRD
3rd rowhttp://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=e3a5eabb8aa7d710VgnVCM1000001d4a900aRCRD
4th rowhttp://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=e95685f23df6d710VgnVCM2000001f4a900aRCRD
5th rowhttp://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=83c3eabb8aa7d710VgnVCM1000001d4a900aRCRD
ValueCountFrequency (%)
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=ce926545f951c010VgnVCM2000000c205a0aRCRD1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=8b75ba61e681c010VgnVCM1000000b205a0aRCRD1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=b4e75a0f8161c010VgnVCM1000000b205a0aRCRD1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=4e3772081661c010VgnVCM1000000b205a0aRCRD1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=c4f06da0aa81c010VgnVCM1000000b205a0aRCRD1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=9a8fd4985261c010VgnVCM1000000b205a0aRCRD1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=6745b7a3621ed210VgnVCM2000000c205a0aRCRD1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=4443d788acccd210VgnVCM2000000c205a0aRCRD1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=137c760a581a7610VgnVCM1000001d4a900aRCRD1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=8abc444a2951c010VgnVCM2000000c205a0aRCRD1
 
0.6%
Other values (162)162
94.2%
2022-10-08T15:06:40.109666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410vgnvcm100000171f5a0arcrd&vgnextoid=ce926545f951c010vgnvcm2000000c205a0arcrd1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410vgnvcm100000171f5a0arcrd&vgnextoid=8b75ba61e681c010vgnvcm1000000b205a0arcrd1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410vgnvcm100000171f5a0arcrd&vgnextoid=b4e75a0f8161c010vgnvcm1000000b205a0arcrd1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410vgnvcm100000171f5a0arcrd&vgnextoid=4e3772081661c010vgnvcm1000000b205a0arcrd1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410vgnvcm100000171f5a0arcrd&vgnextoid=c4f06da0aa81c010vgnvcm1000000b205a0arcrd1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410vgnvcm100000171f5a0arcrd&vgnextoid=9a8fd4985261c010vgnvcm1000000b205a0arcrd1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410vgnvcm100000171f5a0arcrd&vgnextoid=6745b7a3621ed210vgnvcm2000000c205a0arcrd1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410vgnvcm100000171f5a0arcrd&vgnextoid=4443d788acccd210vgnvcm2000000c205a0arcrd1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410vgnvcm100000171f5a0arcrd&vgnextoid=137c760a581a7610vgnvcm1000001d4a900arcrd1
 
0.6%
http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410vgnvcm100000171f5a0arcrd&vgnextoid=8abc444a2951c010vgnvcm2000000c205a0arcrd1
 
0.6%
Other values (162)162
94.2%

Most occurring characters

ValueCountFrequency (%)
02922
 
11.8%
a1500
 
6.1%
11224
 
4.9%
n1204
 
4.9%
e1141
 
4.6%
d1051
 
4.2%
b906
 
3.7%
t860
 
3.5%
/860
 
3.5%
4689
 
2.8%
Other values (32)12411
50.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12714
51.3%
Decimal Number7066
28.5%
Uppercase Letter2752
 
11.1%
Other Punctuation1892
 
7.6%
Math Symbol344
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
a1500
11.8%
n1204
 
9.5%
e1141
 
9.0%
d1051
 
8.3%
b906
 
7.1%
t860
 
6.8%
i688
 
5.4%
s688
 
5.4%
g688
 
5.4%
w516
 
4.1%
Other values (11)3472
27.3%
ValueCountFrequency (%)
02922
41.4%
11224
17.3%
4689
 
9.8%
5456
 
6.5%
2364
 
5.2%
8351
 
5.0%
7342
 
4.8%
6320
 
4.5%
3274
 
3.9%
9124
 
1.8%
ValueCountFrequency (%)
/860
45.5%
.516
27.3%
:172
 
9.1%
?172
 
9.1%
&172
 
9.1%
ValueCountFrequency (%)
V688
25.0%
C688
25.0%
R688
25.0%
M344
12.5%
D344
12.5%
ValueCountFrequency (%)
=344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15466
62.4%
Common9302
37.6%

Most frequent character per script

ValueCountFrequency (%)
a1500
 
9.7%
n1204
 
7.8%
e1141
 
7.4%
d1051
 
6.8%
b906
 
5.9%
t860
 
5.6%
i688
 
4.4%
s688
 
4.4%
g688
 
4.4%
V688
 
4.4%
Other values (16)6052
39.1%
ValueCountFrequency (%)
02922
31.4%
11224
13.2%
/860
 
9.2%
4689
 
7.4%
.516
 
5.5%
5456
 
4.9%
2364
 
3.9%
8351
 
3.8%
=344
 
3.7%
7342
 
3.7%
Other values (6)1234
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII24768
100.0%

Most frequent character per block

ValueCountFrequency (%)
02922
 
11.8%
a1500
 
6.1%
11224
 
4.9%
n1204
 
4.9%
e1141
 
4.6%
d1051
 
4.2%
b906
 
3.7%
t860
 
3.5%
/860
 
3.5%
4689
 
2.8%
Other values (32)12411
50.1%

NOMBRE-VIA
Categorical

HIGH CARDINALITY
UNIFORM

Distinct154
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
GENERAL RICARDOS
 
3
DOCTOR ESQUERDO
 
3
VICTORIA
 
3
ULISES
 
3
COLMENAR VIEJO
 
2
Other values (149)
158 

Length

Max length38
Median length13
Mean length13.1744186
Min length3

Characters and Unicode

Total characters2266
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)81.4%

Sample

1st rowJOSE MARTINEZ DE VELASCO
2nd rowJERTE
3rd rowBELZUNEGUI
4th rowLUIS USERA
5th rowINFANTA MERCEDES
ValueCountFrequency (%)
GENERAL RICARDOS3
 
1.7%
DOCTOR ESQUERDO3
 
1.7%
VICTORIA3
 
1.7%
ULISES3
 
1.7%
COLMENAR VIEJO2
 
1.2%
AGUSTIN CALVO2
 
1.2%
ARTURO SORIA2
 
1.2%
ZAZUAR2
 
1.2%
DUQUESA DE CASTREJON2
 
1.2%
SAN BERNARDO2
 
1.2%
Other values (144)148
86.0%
2022-10-08T15:06:40.372255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de37
 
10.5%
santa6
 
1.7%
doctor6
 
1.7%
victoria5
 
1.4%
la5
 
1.4%
los3
 
0.9%
francisco3
 
0.9%
ricardos3
 
0.9%
general3
 
0.9%
juan3
 
0.9%
Other values (232)277
78.9%

Most occurring characters

ValueCountFrequency (%)
A320
14.1%
E211
 
9.3%
R195
 
8.6%
179
 
7.9%
O174
 
7.7%
I141
 
6.2%
S130
 
5.7%
N130
 
5.7%
L117
 
5.2%
D107
 
4.7%
Other values (34)562
24.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2050
90.5%
Space Separator179
 
7.9%
Lowercase Letter24
 
1.1%
Other Punctuation9
 
0.4%
Decimal Number3
 
0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A320
15.6%
E211
10.3%
R195
9.5%
O174
 
8.5%
I141
 
6.9%
S130
 
6.3%
N130
 
6.3%
L117
 
5.7%
D107
 
5.2%
C87
 
4.2%
Other values (19)438
21.4%
ValueCountFrequency (%)
a3
12.5%
m3
12.5%
p3
12.5%
t3
12.5%
i3
12.5%
l3
12.5%
d3
12.5%
e3
12.5%
ValueCountFrequency (%)
61
33.3%
01
33.3%
71
33.3%
ValueCountFrequency (%)
;6
66.7%
&3
33.3%
ValueCountFrequency (%)
179
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2074
91.5%
Common192
 
8.5%

Most frequent character per script

ValueCountFrequency (%)
A320
15.4%
E211
10.2%
R195
9.4%
O174
 
8.4%
I141
 
6.8%
S130
 
6.3%
N130
 
6.3%
L117
 
5.6%
D107
 
5.2%
C87
 
4.2%
Other values (27)462
22.3%
ValueCountFrequency (%)
179
93.2%
;6
 
3.1%
&3
 
1.6%
-1
 
0.5%
61
 
0.5%
01
 
0.5%
71
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2250
99.3%
Latin 1 Sup16
 
0.7%

Most frequent character per block

ValueCountFrequency (%)
A320
14.2%
E211
 
9.4%
R195
 
8.7%
179
 
8.0%
O174
 
7.7%
I141
 
6.3%
S130
 
5.8%
N130
 
5.8%
L117
 
5.2%
D107
 
4.8%
Other values (30)546
24.3%
ValueCountFrequency (%)
Ñ8
50.0%
Í5
31.2%
Á2
 
12.5%
Ó1
 
6.2%

CLASE-VIAL
Categorical

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
CALLE
143 
AVENIDA
18 
CARRETERA
 
5
PASEO
 
5
PLAZA
 
1

Length

Max length9
Median length5
Mean length5.325581395
Min length5

Characters and Unicode

Total characters916
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowCALLE
2nd rowCALLE
3rd rowCALLE
4th rowCALLE
5th rowCALLE
ValueCountFrequency (%)
CALLE143
83.1%
AVENIDA18
 
10.5%
CARRETERA5
 
2.9%
PASEO5
 
2.9%
PLAZA1
 
0.6%
2022-10-08T15:06:40.586289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:40.663254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
calle143
83.1%
avenida18
 
10.5%
carretera5
 
2.9%
paseo5
 
2.9%
plaza1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
L287
31.3%
A196
21.4%
E176
19.2%
C148
16.2%
V18
 
2.0%
N18
 
2.0%
I18
 
2.0%
D18
 
2.0%
R15
 
1.6%
P6
 
0.7%
Other values (4)16
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter916
100.0%

Most frequent character per category

ValueCountFrequency (%)
L287
31.3%
A196
21.4%
E176
19.2%
C148
16.2%
V18
 
2.0%
N18
 
2.0%
I18
 
2.0%
D18
 
2.0%
R15
 
1.6%
P6
 
0.7%
Other values (4)16
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin916
100.0%

Most frequent character per script

ValueCountFrequency (%)
L287
31.3%
A196
21.4%
E176
19.2%
C148
16.2%
V18
 
2.0%
N18
 
2.0%
I18
 
2.0%
D18
 
2.0%
R15
 
1.6%
P6
 
0.7%
Other values (4)16
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII916
100.0%

Most frequent character per block

ValueCountFrequency (%)
L287
31.3%
A196
21.4%
E176
19.2%
C148
16.2%
V18
 
2.0%
N18
 
2.0%
I18
 
2.0%
D18
 
2.0%
R15
 
1.6%
P6
 
0.7%
Other values (4)16
 
1.7%

TIPO-NUM
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
V
140 
NUM
31 
T
 
1

Length

Max length3
Median length1
Mean length1.360465116
Min length1

Characters and Unicode

Total characters234
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowV
2nd rowV
3rd rowV
4th rowV
5th rowV
ValueCountFrequency (%)
V140
81.4%
NUM31
 
18.0%
T1
 
0.6%
2022-10-08T15:06:40.898255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:40.971256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
v140
81.4%
num31
 
18.0%
t1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
V140
59.8%
N31
 
13.2%
U31
 
13.2%
M31
 
13.2%
T1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter234
100.0%

Most frequent character per category

ValueCountFrequency (%)
V140
59.8%
N31
 
13.2%
U31
 
13.2%
M31
 
13.2%
T1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin234
100.0%

Most frequent character per script

ValueCountFrequency (%)
V140
59.8%
N31
 
13.2%
U31
 
13.2%
M31
 
13.2%
T1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ValueCountFrequency (%)
V140
59.8%
N31
 
13.2%
U31
 
13.2%
M31
 
13.2%
T1
 
0.4%

NUM
Real number (ℝ≥0)

Distinct79
Distinct (%)46.2%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean55.04678363
Minimum1
Maximum1600
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2022-10-08T15:06:41.054921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median24
Q355
95-th percentile182.5
Maximum1600
Range1599
Interquartile range (IQR)48

Descriptive statistics

Standard deviation133.5451372
Coefficient of variation (CV)2.426029796
Kurtosis106.4917405
Mean55.04678363
Median Absolute Deviation (MAD)20
Skewness9.405494577
Sum9413
Variance17834.30368
MonotocityNot monotonic
2022-10-08T15:06:41.176904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49
 
5.2%
39
 
5.2%
28
 
4.7%
17
 
4.1%
385
 
2.9%
155
 
2.9%
75
 
2.9%
145
 
2.9%
245
 
2.9%
85
 
2.9%
Other values (69)108
62.8%
ValueCountFrequency (%)
17
4.1%
28
4.7%
39
5.2%
49
5.2%
52
 
1.2%
ValueCountFrequency (%)
16001
0.6%
3841
0.6%
2781
0.6%
2611
0.6%
2471
0.6%

PLANTA
Categorical

MISSING

Distinct5
Distinct (%)83.3%
Missing166
Missing (%)96.5%
Memory size1.5 KiB
BAJA
2
Bajo
5

Length

Max length4
Median length3
Mean length2.666666667
Min length1

Characters and Unicode

Total characters16
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)66.7%

Sample

1st row2
2nd rowBajo
3rd row5
4th row
5th rowBAJA
ValueCountFrequency (%)
BAJA2
 
1.2%
21
 
0.6%
Bajo1
 
0.6%
51
 
0.6%
1
 
0.6%
(Missing)166
96.5%
2022-10-08T15:06:41.382516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:41.446497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
baja2
33.3%
21
16.7%
bajo1
16.7%
51
16.7%
1
16.7%

Most occurring characters

ValueCountFrequency (%)
A4
25.0%
B3
18.8%
22
12.5%
J2
12.5%
a1
 
6.2%
j1
 
6.2%
o1
 
6.2%
51
 
6.2%
º1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9
56.2%
Decimal Number3
 
18.8%
Lowercase Letter3
 
18.8%
Other Letter1
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
A4
44.4%
B3
33.3%
J2
22.2%
ValueCountFrequency (%)
a1
33.3%
j1
33.3%
o1
33.3%
ValueCountFrequency (%)
22
66.7%
51
33.3%
ValueCountFrequency (%)
º1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13
81.2%
Common3
 
18.8%

Most frequent character per script

ValueCountFrequency (%)
A4
30.8%
B3
23.1%
J2
15.4%
a1
 
7.7%
j1
 
7.7%
o1
 
7.7%
º1
 
7.7%
ValueCountFrequency (%)
22
66.7%
51
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15
93.8%
Latin 1 Sup1
 
6.2%

Most frequent character per block

ValueCountFrequency (%)
A4
26.7%
B3
20.0%
22
13.3%
J2
13.3%
a1
 
6.7%
j1
 
6.7%
o1
 
6.7%
51
 
6.7%
ValueCountFrequency (%)
º1
100.0%

PUERTA
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing171
Missing (%)99.4%
Memory size1.5 KiB
A

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowA
ValueCountFrequency (%)
A1
 
0.6%
(Missing)171
99.4%
2022-10-08T15:06:41.593364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:41.647362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
a1
100.0%

Most occurring characters

ValueCountFrequency (%)
A1
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1
100.0%

Most frequent character per category

ValueCountFrequency (%)
A1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1
100.0%

Most frequent character per script

ValueCountFrequency (%)
A1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1
100.0%

Most frequent character per block

ValueCountFrequency (%)
A1
100.0%

ESCALERAS
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing172
Missing (%)100.0%
Memory size1.5 KiB

ORIENTACION
Categorical

MISSING
UNIFORM

Distinct7
Distinct (%)100.0%
Missing165
Missing (%)95.9%
Memory size1.5 KiB
A
KILÓMETRO 13
CHALET
km 14,400
y 5
Other values (2)

Length

Max length25
Median length6
Mean length8.571428571
Min length1

Characters and Unicode

Total characters60
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowA
2nd rowKILÓMETRO 13
3rd rowCHALET
4th rowkm 14,400
5th rowy 5
ValueCountFrequency (%)
A1
 
0.6%
KILÓMETRO 131
 
0.6%
CHALET1
 
0.6%
km 14,4001
 
0.6%
y 51
 
0.6%
Y 461
 
0.6%
con vuelta a calle Templo1
 
0.6%
(Missing)165
95.9%
2022-10-08T15:06:41.795363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:41.860368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
a2
13.3%
y2
13.3%
kilómetro1
 
6.7%
131
 
6.7%
chalet1
 
6.7%
km1
 
6.7%
14,4001
 
6.7%
51
 
6.7%
461
 
6.7%
con1
 
6.7%
Other values (3)3
20.0%

Most occurring characters

ValueCountFrequency (%)
8
 
13.3%
l4
 
6.7%
T3
 
5.0%
43
 
5.0%
e3
 
5.0%
a3
 
5.0%
A2
 
3.3%
L2
 
3.3%
E2
 
3.3%
12
 
3.3%
Other values (24)28
46.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23
38.3%
Uppercase Letter18
30.0%
Decimal Number10
16.7%
Space Separator8
 
13.3%
Other Punctuation1
 
1.7%

Most frequent character per category

ValueCountFrequency (%)
T3
16.7%
A2
11.1%
L2
11.1%
E2
11.1%
K1
 
5.6%
I1
 
5.6%
Ó1
 
5.6%
M1
 
5.6%
R1
 
5.6%
O1
 
5.6%
Other values (3)3
16.7%
ValueCountFrequency (%)
l4
17.4%
e3
13.0%
a3
13.0%
m2
8.7%
c2
8.7%
o2
8.7%
k1
 
4.3%
y1
 
4.3%
n1
 
4.3%
v1
 
4.3%
Other values (3)3
13.0%
ValueCountFrequency (%)
43
30.0%
12
20.0%
02
20.0%
31
 
10.0%
51
 
10.0%
61
 
10.0%
ValueCountFrequency (%)
8
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin41
68.3%
Common19
31.7%

Most frequent character per script

ValueCountFrequency (%)
l4
 
9.8%
T3
 
7.3%
e3
 
7.3%
a3
 
7.3%
A2
 
4.9%
L2
 
4.9%
E2
 
4.9%
m2
 
4.9%
c2
 
4.9%
o2
 
4.9%
Other values (16)16
39.0%
ValueCountFrequency (%)
8
42.1%
43
 
15.8%
12
 
10.5%
02
 
10.5%
31
 
5.3%
,1
 
5.3%
51
 
5.3%
61
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII59
98.3%
Latin 1 Sup1
 
1.7%

Most frequent character per block

ValueCountFrequency (%)
8
 
13.6%
l4
 
6.8%
T3
 
5.1%
43
 
5.1%
e3
 
5.1%
a3
 
5.1%
A2
 
3.4%
L2
 
3.4%
E2
 
3.4%
12
 
3.4%
Other values (23)27
45.8%
ValueCountFrequency (%)
Ó1
100.0%

LOCALIDAD
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
MADRID
172 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1032
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMADRID
2nd rowMADRID
3rd rowMADRID
4th rowMADRID
5th rowMADRID
ValueCountFrequency (%)
MADRID172
100.0%
2022-10-08T15:06:42.029464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:42.084059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
madrid172
100.0%

Most occurring characters

ValueCountFrequency (%)
D344
33.3%
M172
16.7%
A172
16.7%
R172
16.7%
I172
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1032
100.0%

Most frequent character per category

ValueCountFrequency (%)
D344
33.3%
M172
16.7%
A172
16.7%
R172
16.7%
I172
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1032
100.0%

Most frequent character per script

ValueCountFrequency (%)
D344
33.3%
M172
16.7%
A172
16.7%
R172
16.7%
I172
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1032
100.0%

Most frequent character per block

ValueCountFrequency (%)
D344
33.3%
M172
16.7%
A172
16.7%
R172
16.7%
I172
16.7%

PROVINCIA
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
MADRID
172 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1032
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMADRID
2nd rowMADRID
3rd rowMADRID
4th rowMADRID
5th rowMADRID
ValueCountFrequency (%)
MADRID172
100.0%
2022-10-08T15:06:42.222708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:42.280343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
madrid172
100.0%

Most occurring characters

ValueCountFrequency (%)
D344
33.3%
M172
16.7%
A172
16.7%
R172
16.7%
I172
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1032
100.0%

Most frequent character per category

ValueCountFrequency (%)
D344
33.3%
M172
16.7%
A172
16.7%
R172
16.7%
I172
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1032
100.0%

Most frequent character per script

ValueCountFrequency (%)
D344
33.3%
M172
16.7%
A172
16.7%
R172
16.7%
I172
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1032
100.0%

Most frequent character per block

ValueCountFrequency (%)
D344
33.3%
M172
16.7%
A172
16.7%
R172
16.7%
I172
16.7%

CODIGO-POSTAL
Real number (ℝ≥0)

Distinct47
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28028.15698
Minimum28001
Maximum28053
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2022-10-08T15:06:42.365341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum28001
5-th percentile28003
Q128019
median28030
Q328041
95-th percentile28047.9
Maximum28053
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.31450449
Coefficient of variation (CV)0.0005107187212
Kurtosis-0.9534771128
Mean28028.15698
Median Absolute Deviation (MAD)11
Skewness-0.3650461657
Sum4820843
Variance204.9050388
MonotocityNot monotonic
2022-10-08T15:06:42.532340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2804312
 
7.0%
2802312
 
7.0%
280359
 
5.2%
280447
 
4.1%
280026
 
3.5%
280286
 
3.5%
280075
 
2.9%
280085
 
2.9%
280365
 
2.9%
280405
 
2.9%
Other values (37)100
58.1%
ValueCountFrequency (%)
280012
 
1.2%
280026
3.5%
280033
1.7%
280042
 
1.2%
280054
2.3%
ValueCountFrequency (%)
280532
1.2%
280511
 
0.6%
280502
1.2%
280494
2.3%
280474
2.3%

COD-BARRIO
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)5.4%
Missing6
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean3.897590361
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2022-10-08T15:06:42.636340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.943534793
Coefficient of variation (CV)0.4986503488
Kurtosis-0.6612227267
Mean3.897590361
Median Absolute Deviation (MAD)1
Skewness0.2963584877
Sum647
Variance3.777327492
MonotocityNot monotonic
2022-10-08T15:06:42.720340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
335
20.3%
525
14.5%
425
14.5%
222
12.8%
121
12.2%
620
11.6%
714
 
8.1%
82
 
1.2%
92
 
1.2%
(Missing)6
 
3.5%
ValueCountFrequency (%)
121
12.2%
222
12.8%
335
20.3%
425
14.5%
525
14.5%
ValueCountFrequency (%)
92
 
1.2%
82
 
1.2%
714
8.1%
620
11.6%
525
14.5%

BARRIO
Categorical

HIGH CARDINALITY
MISSING

Distinct87
Distinct (%)51.2%
Missing2
Missing (%)1.2%
Memory size1.5 KiB
CIUDAD UNIVERSITARIA
 
9
CANILLAS
 
7
EL PLANTIO
 
5
MIRASIERRA
 
5
VALDEMARIN
 
5
Other values (82)
139 

Length

Max length20
Median length10
Mean length10.45882353
Min length4

Characters and Unicode

Total characters1778
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)25.9%

Sample

1st rowESTRELLA
2nd rowPALACIO
3rd rowPUERTA BONITA
4th rowPRADOLONGO
5th rowCUATRO CAMINOS
ValueCountFrequency (%)
CIUDAD UNIVERSITARIA9
 
5.2%
CANILLAS7
 
4.1%
EL PLANTIO5
 
2.9%
MIRASIERRA5
 
2.9%
VALDEMARIN5
 
2.9%
PUERTA BONITA5
 
2.9%
BUENAVISTA4
 
2.3%
HISPANOAMERICA4
 
2.3%
PAVONES4
 
2.3%
NUEVA ESPAÑA4
 
2.3%
Other values (77)118
68.6%
2022-10-08T15:06:42.974340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ciudad11
 
4.5%
el9
 
3.7%
universitaria9
 
3.7%
san7
 
2.9%
canillas7
 
2.9%
valdemarin5
 
2.0%
plantio5
 
2.0%
mirasierra5
 
2.0%
puerta5
 
2.0%
bonita5
 
2.0%
Other values (102)177
72.2%

Most occurring characters

ValueCountFrequency (%)
A299
16.8%
E163
 
9.2%
I147
 
8.3%
R132
 
7.4%
S130
 
7.3%
L129
 
7.3%
N100
 
5.6%
O92
 
5.2%
75
 
4.2%
U70
 
3.9%
Other values (18)441
24.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1697
95.4%
Space Separator75
 
4.2%
Other Punctuation6
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
A299
17.6%
E163
9.6%
I147
8.7%
R132
 
7.8%
S130
 
7.7%
L129
 
7.6%
N100
 
5.9%
O92
 
5.4%
U70
 
4.1%
D69
 
4.1%
Other values (16)366
21.6%
ValueCountFrequency (%)
75
100.0%
ValueCountFrequency (%)
.6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1697
95.4%
Common81
 
4.6%

Most frequent character per script

ValueCountFrequency (%)
A299
17.6%
E163
9.6%
I147
8.7%
R132
 
7.8%
S130
 
7.7%
L129
 
7.6%
N100
 
5.9%
O92
 
5.4%
U70
 
4.1%
D69
 
4.1%
Other values (16)366
21.6%
ValueCountFrequency (%)
75
92.6%
.6
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1767
99.4%
Latin 1 Sup11
 
0.6%

Most frequent character per block

ValueCountFrequency (%)
A299
16.9%
E163
 
9.2%
I147
 
8.3%
R132
 
7.5%
S130
 
7.4%
L129
 
7.3%
N100
 
5.7%
O92
 
5.2%
75
 
4.2%
U70
 
4.0%
Other values (15)430
24.3%
ValueCountFrequency (%)
Ñ9
81.8%
Ü1
 
9.1%
Á1
 
9.1%

COD-DISTRITO
Real number (ℝ≥0)

MISSING

Distinct21
Distinct (%)12.7%
Missing6
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean10.06024096
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2022-10-08T15:06:43.072874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median9
Q314
95-th percentile19.75
Maximum21
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.148343417
Coefficient of variation (CV)0.5117515014
Kurtosis-0.7718488592
Mean10.06024096
Median Absolute Deviation (MAD)4
Skewness0.2532579325
Sum1670
Variance26.50543994
MonotocityNot monotonic
2022-10-08T15:06:43.163874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
926
15.1%
1615
 
8.7%
515
 
8.7%
1113
 
7.6%
812
 
7.0%
410
 
5.8%
27
 
4.1%
137
 
4.1%
126
 
3.5%
106
 
3.5%
Other values (11)49
28.5%
ValueCountFrequency (%)
14
 
2.3%
27
4.1%
35
 
2.9%
410
5.8%
515
8.7%
ValueCountFrequency (%)
213
1.7%
206
3.5%
191
 
0.6%
185
2.9%
174
2.3%

DISTRITO
Categorical

Distinct21
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
MONCLOA-ARAVACA
27 
CHAMARTIN
15 
HORTALEZA
15 
FUENCARRAL-EL PARDO
14 
CARABANCHEL
13 
Other values (16)
88 

Length

Max length19
Median length10
Mean length11.50581395
Min length5

Characters and Unicode

Total characters1979
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowRETIRO
2nd rowCENTRO
3rd rowCARABANCHEL
4th rowUSERA
5th rowTETUAN
ValueCountFrequency (%)
MONCLOA-ARAVACA27
15.7%
CHAMARTIN15
 
8.7%
HORTALEZA15
 
8.7%
FUENCARRAL-EL PARDO14
 
8.1%
CARABANCHEL13
 
7.6%
SALAMANCA11
 
6.4%
PUENTE DE VALLECAS7
 
4.1%
ARGANZUELA7
 
4.1%
LATINA7
 
4.1%
CIUDAD LINEAL6
 
3.5%
Other values (11)50
29.1%
2022-10-08T15:06:43.382416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moncloa-aravaca27
 
12.2%
hortaleza15
 
6.8%
chamartin15
 
6.8%
fuencarral-el14
 
6.3%
pardo14
 
6.3%
carabanchel13
 
5.9%
vallecas12
 
5.4%
de12
 
5.4%
salamanca11
 
5.0%
puente7
 
3.2%
Other values (16)82
36.9%

Most occurring characters

ValueCountFrequency (%)
A467
23.6%
L188
9.5%
R159
 
8.0%
C155
 
7.8%
E148
 
7.5%
N127
 
6.4%
O98
 
5.0%
T66
 
3.3%
M64
 
3.2%
I62
 
3.1%
Other values (13)445
22.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1882
95.1%
Space Separator50
 
2.5%
Dash Punctuation47
 
2.4%

Most frequent character per category

ValueCountFrequency (%)
A467
24.8%
L188
10.0%
R159
 
8.4%
C155
 
8.2%
E148
 
7.9%
N127
 
6.7%
O98
 
5.2%
T66
 
3.5%
M64
 
3.4%
I62
 
3.3%
Other values (11)348
18.5%
ValueCountFrequency (%)
-47
100.0%
ValueCountFrequency (%)
50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1882
95.1%
Common97
 
4.9%

Most frequent character per script

ValueCountFrequency (%)
A467
24.8%
L188
10.0%
R159
 
8.4%
C155
 
8.2%
E148
 
7.9%
N127
 
6.7%
O98
 
5.2%
T66
 
3.5%
M64
 
3.4%
I62
 
3.3%
Other values (11)348
18.5%
ValueCountFrequency (%)
50
51.5%
-47
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1979
100.0%

Most frequent character per block

ValueCountFrequency (%)
A467
23.6%
L188
9.5%
R159
 
8.0%
C155
 
7.8%
E148
 
7.5%
N127
 
6.4%
O98
 
5.0%
T66
 
3.3%
M64
 
3.2%
I62
 
3.1%
Other values (13)445
22.5%

COORDENADA-X
Real number (ℝ≥0)

HIGH CORRELATION

Distinct168
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441188.9942
Minimum429102
Maximum450373
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2022-10-08T15:06:43.502416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum429102
5-th percentile433727.85
Q1438948.5
median441557.5
Q3443742.25
95-th percentile448055.3
Maximum450373
Range21271
Interquartile range (IQR)4793.75

Descriptive statistics

Standard deviation4214.901531
Coefficient of variation (CV)0.00955350561
Kurtosis0.3317387041
Mean441188.9942
Median Absolute Deviation (MAD)2492.5
Skewness-0.4000755336
Sum75884507
Variance17765394.92
MonotocityNot monotonic
2022-10-08T15:06:43.666430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4374873
 
1.7%
4448172
 
1.2%
4417012
 
1.2%
4433461
 
0.6%
4385891
 
0.6%
4464861
 
0.6%
4403281
 
0.6%
4422041
 
0.6%
4396611
 
0.6%
4424331
 
0.6%
Other values (158)158
91.9%
ValueCountFrequency (%)
4291021
0.6%
4294071
0.6%
4299491
0.6%
4304471
0.6%
4310211
0.6%
ValueCountFrequency (%)
4503731
0.6%
4502671
0.6%
4499351
0.6%
4487741
0.6%
4484781
0.6%

COORDENADA-Y
Real number (ℝ≥0)

HIGH CORRELATION

Distinct169
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4475966.558
Minimum4466215
Maximum4490699
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2022-10-08T15:06:43.850416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4466215
5-th percentile4469204.95
Q14472269.25
median4476213.5
Q34479376
95-th percentile4482147.9
Maximum4490699
Range24484
Interquartile range (IQR)7106.75

Descriptive statistics

Standard deviation4581.76748
Coefficient of variation (CV)0.001023637559
Kurtosis0.1597502634
Mean4475966.558
Median Absolute Deviation (MAD)3497.5
Skewness0.238483283
Sum769866248
Variance20992593.24
MonotocityNot monotonic
2022-10-08T15:06:43.994415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44707733
 
1.7%
44723872
 
1.2%
44739921
 
0.6%
44789621
 
0.6%
44731601
 
0.6%
44800891
 
0.6%
44727081
 
0.6%
44685321
 
0.6%
44783101
 
0.6%
44906991
 
0.6%
Other values (159)159
92.4%
ValueCountFrequency (%)
44662151
0.6%
44671031
0.6%
44671691
0.6%
44675291
0.6%
44679591
0.6%
ValueCountFrequency (%)
44906991
0.6%
44904911
0.6%
44879601
0.6%
44878451
0.6%
44836891
0.6%

LATITUD
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct169
Distinct (%)99.4%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean40.43058511
Minimum40.34450741
Maximum40.5395209
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2022-10-08T15:06:44.132214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum40.34450741
5-th percentile40.37092803
Q140.39812638
median40.43330771
Q340.46136819
95-th percentile40.48465855
Maximum40.5395209
Range0.195013496
Interquartile range (IQR)0.06324180875

Descriptive statistics

Standard deviation0.03891513595
Coefficient of variation (CV)0.0009625172586
Kurtosis-0.5803725334
Mean40.43058511
Median Absolute Deviation (MAD)0.03147621776
Skewness-0.05579441006
Sum6873.199469
Variance0.001514387806
MonotocityNot monotonic
2022-10-08T15:06:44.263792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.385197382
 
1.2%
40.452643681
 
0.6%
40.468722441
 
0.6%
40.401383861
 
0.6%
40.365226581
 
0.6%
40.453435091
 
0.6%
40.450090831
 
0.6%
40.459845131
 
0.6%
40.467934121
 
0.6%
40.461445461
 
0.6%
Other values (159)159
92.4%
(Missing)2
 
1.2%
ValueCountFrequency (%)
40.344507411
0.6%
40.352253541
0.6%
40.353035921
0.6%
40.356281261
0.6%
40.35999541
0.6%
ValueCountFrequency (%)
40.53952091
0.6%
40.539321221
0.6%
40.502049441
0.6%
40.494560571
0.6%
40.493100121
0.6%

LONGITUD
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct169
Distinct (%)99.4%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean-3.693217825
Minimum-3.8364014
Maximum-3.585469238
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2022-10-08T15:06:44.395793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3.8364014
5-th percentile-3.781867786
Q1-3.719381953
median-3.688865547
Q3-3.662881012
95-th percentile-3.612076715
Maximum-3.585469238
Range0.2509321625
Interquartile range (IQR)0.05650094081

Descriptive statistics

Standard deviation0.04994757264
Coefficient of variation (CV)-0.01352413397
Kurtosis0.3324535279
Mean-3.693217825
Median Absolute Deviation (MAD)0.02958254169
Skewness-0.4150915812
Sum-627.8470303
Variance0.002494760013
MonotocityNot monotonic
2022-10-08T15:06:44.521792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.7364997412
 
1.2%
-3.6532914171
 
0.6%
-3.7972290041
 
0.6%
-3.6319256291
 
0.6%
-3.7028211161
 
0.6%
-3.6816149851
 
0.6%
-3.6788806461
 
0.6%
-3.58657131
 
0.6%
-3.7952733641
 
0.6%
-3.7243458191
 
0.6%
Other values (159)159
92.4%
(Missing)2
 
1.2%
ValueCountFrequency (%)
-3.83640141
0.6%
-3.8327841461
0.6%
-3.8263606691
0.6%
-3.8204507021
0.6%
-3.8137196181
0.6%
ValueCountFrequency (%)
-3.5854692381
0.6%
-3.58657131
0.6%
-3.5905522631
0.6%
-3.6038580611
0.6%
-3.6069827611
0.6%

TELEFONO
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct166
Distinct (%)97.6%
Missing2
Missing (%)1.2%
Memory size1.5 KiB
913 287 070
 
2
913 728 102
 
2
914 625 800
 
2
913 648 252
 
2
917 400 386
 
1
Other values (161)
161 

Length

Max length54
Median length11
Mean length13.73529412
Min length11

Characters and Unicode

Total characters2335
Distinct characters26
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique162 ?
Unique (%)95.3%

Sample

1st row915 571 319
2nd row913 644 005
3rd row915 254 211
4th row914 766 831
5th row913 308 842
ValueCountFrequency (%)
913 287 0702
 
1.2%
913 728 1022
 
1.2%
914 625 8002
 
1.2%
913 648 2522
 
1.2%
917 400 3861
 
0.6%
913 079 3401
 
0.6%
913 710 867 / 913 710 1581
 
0.6%
913 175 467 / 913 175 4681
 
0.6%
913 440 0201
 
0.6%
917 347 4771
 
0.6%
Other values (156)156
90.7%
(Missing)2
 
1.2%
2022-10-08T15:06:44.781792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
91367
 
10.8%
91745
 
7.3%
91538
 
6.1%
91431
 
5.0%
22
 
3.6%
día5
 
0.8%
centro5
 
0.8%
residencia5
 
0.8%
8005
 
0.8%
2525
 
0.8%
Other values (301)391
63.2%

Most occurring characters

ValueCountFrequency (%)
452
19.4%
1295
12.6%
9273
11.7%
0198
8.5%
3161
 
6.9%
5152
 
6.5%
4149
 
6.4%
7142
 
6.1%
2136
 
5.8%
6121
 
5.2%
Other values (16)256
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1728
74.0%
Space Separator452
 
19.4%
Lowercase Letter111
 
4.8%
Other Punctuation22
 
0.9%
Open Punctuation11
 
0.5%
Close Punctuation11
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
e21
18.9%
d15
13.5%
n11
9.9%
c11
9.9%
r10
9.0%
i10
9.0%
a10
9.0%
o6
 
5.4%
s6
 
5.4%
t5
 
4.5%
Other values (2)6
 
5.4%
ValueCountFrequency (%)
1295
17.1%
9273
15.8%
0198
11.5%
3161
9.3%
5152
8.8%
4149
8.6%
7142
8.2%
2136
7.9%
6121
7.0%
8101
 
5.8%
ValueCountFrequency (%)
452
100.0%
ValueCountFrequency (%)
/22
100.0%
ValueCountFrequency (%)
(11
100.0%
ValueCountFrequency (%)
)11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2224
95.2%
Latin111
 
4.8%

Most frequent character per script

ValueCountFrequency (%)
452
20.3%
1295
13.3%
9273
12.3%
0198
8.9%
3161
 
7.2%
5152
 
6.8%
4149
 
6.7%
7142
 
6.4%
2136
 
6.1%
6121
 
5.4%
Other values (4)145
 
6.5%
ValueCountFrequency (%)
e21
18.9%
d15
13.5%
n11
9.9%
c11
9.9%
r10
9.0%
i10
9.0%
a10
9.0%
o6
 
5.4%
s6
 
5.4%
t5
 
4.5%
Other values (2)6
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2330
99.8%
Latin 1 Sup5
 
0.2%

Most frequent character per block

ValueCountFrequency (%)
452
19.4%
1295
12.7%
9273
11.7%
0198
8.5%
3161
 
6.9%
5152
 
6.5%
4149
 
6.4%
7142
 
6.1%
2136
 
5.8%
6121
 
5.2%
Other values (15)251
10.8%
ValueCountFrequency (%)
í5
100.0%

FAX
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing172
Missing (%)100.0%
Memory size1.5 KiB

EMAIL
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing172
Missing (%)100.0%
Memory size1.5 KiB

TIPO
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
170 
/contenido/entidadesYorganismos/CentrosAtencionMedica/HospitalesClinicasSanatorios
 
1
/contenido/entidadesYorganismos/CentrosAtencionSocial/AlberguesSocialesCentrosAcogida
 
1

Length

Max length91
Median length91
Mean length90.9127907
Min length82

Characters and Unicode

Total characters15637
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st row/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
2nd row/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
3rd row/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
4th row/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
5th row/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
ValueCountFrequency (%)
/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores170
98.8%
/contenido/entidadesYorganismos/CentrosAtencionMedica/HospitalesClinicasSanatorios1
 
0.6%
/contenido/entidadesYorganismos/CentrosAtencionSocial/AlberguesSocialesCentrosAcogida1
 
0.6%
2022-10-08T15:06:44.988792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:45.070473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
contenido/entidadesyorganismos/centrosatencionsocialmayores/residenciasapartamentosmayores170
98.8%
contenido/entidadesyorganismos/centrosatencionmedica/hospitalesclinicassanatorios1
 
0.6%
contenido/entidadesyorganismos/centrosatencionsocial/alberguessocialescentrosacogida1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o1719
11.0%
e1716
11.0%
n1547
9.9%
s1545
9.9%
a1372
8.8%
i1206
 
7.7%
t1031
 
6.6%
r857
 
5.5%
c689
 
4.4%
/688
 
4.4%
Other values (15)3267
20.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13574
86.8%
Uppercase Letter1375
 
8.8%
Other Punctuation688
 
4.4%

Most frequent character per category

ValueCountFrequency (%)
o1719
12.7%
e1716
12.6%
n1547
11.4%
s1545
11.4%
a1372
10.1%
i1206
8.9%
t1031
7.6%
r857
6.3%
c689
5.1%
d688
5.1%
Other values (7)1204
8.9%
ValueCountFrequency (%)
A344
25.0%
M341
24.8%
C174
12.7%
S173
12.6%
Y172
12.5%
R170
12.4%
H1
 
0.1%
ValueCountFrequency (%)
/688
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14949
95.6%
Common688
 
4.4%

Most frequent character per script

ValueCountFrequency (%)
o1719
11.5%
e1716
11.5%
n1547
10.3%
s1545
10.3%
a1372
9.2%
i1206
8.1%
t1031
 
6.9%
r857
 
5.7%
c689
 
4.6%
d688
 
4.6%
Other values (14)2579
17.3%
ValueCountFrequency (%)
/688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15637
100.0%

Most frequent character per block

ValueCountFrequency (%)
o1719
11.0%
e1716
11.0%
n1547
9.9%
s1545
9.9%
a1372
8.8%
i1206
 
7.7%
t1031
 
6.6%
r857
 
5.5%
c689
 
4.4%
/688
 
4.4%
Other values (15)3267
20.9%

Unnamed: 32
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
172 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters172
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
172
100.0%
2022-10-08T15:06:45.280017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-10-08T15:06:45.344017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
172
100.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator172
100.0%

Most frequent character per category

ValueCountFrequency (%)
172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common172
100.0%

Most frequent character per script

ValueCountFrequency (%)
172
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII172
100.0%

Most frequent character per block

ValueCountFrequency (%)
172
100.0%

Interactions

2022-10-08T15:06:28.482775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:28.617777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:28.728777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:28.829774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:28.930774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:29.022775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:29.127383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:29.231382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:29.325994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:29.432982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:29.541983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:29.655989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:29.774994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:29.892982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:29.994986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:30.128544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:30.252131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:30.355132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:30.461131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:30.566130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:30.663130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:30.760133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:30.857130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:30.959130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:31.051711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:31.151711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:31.255273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:31.355274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:31.460272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:31.561273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:31.658273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:31.766274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:31.867276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:31.972273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:32.077857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:32.181858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:32.287441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:32.387440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:32.485440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:32.595455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:32.691442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:32.779440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:32.871468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:32.956442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.048094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.139129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.228128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.318906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.402939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.494907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.591940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.681910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.769908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.860910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:33.953939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:34.045948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:34.129908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:34.228908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:34.334559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:34.430524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:34.528556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:34.629525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:34.724558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:35.214140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:35.321747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:35.425750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:35.521747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:35.616784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:35.715745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:35.813747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:35.899746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-08T15:06:35.982774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-10-08T15:06:45.423019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-08T15:06:45.691024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-08T15:06:45.935021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-08T15:06:46.156613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-08T15:06:36.240391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-08T15:06:37.023035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-08T15:06:37.292851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-08T15:06:37.543854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PKNOMBREDESCRIPCION-ENTIDADHORARIOEQUIPAMIENTOTRANSPORTEDESCRIPCIONACCESIBILIDADCONTENT-URLNOMBRE-VIACLASE-VIALTIPO-NUMNUMPLANTAPUERTAESCALERASORIENTACIONLOCALIDADPROVINCIACODIGO-POSTALCOD-BARRIOBARRIOCOD-DISTRITODISTRITOCOORDENADA-XCOORDENADA-YLATITUDLONGITUDTELEFONOFAXEMAILTIPOUnnamed: 32
023711Apartamentos Municipales para Mayores 'Retiro'NaNNaNAlojamiento.Metro: Sainz de BarandaBus: 15, 30, 56, 143, 202Condiciones de Admisión: - Estar empadronado en el municipio de Madrid.- Ser mayor de 65 años (también el cónyuge que conviva con él, siempre que tenga cumplidos los 60 años).- Ser autónomo en la realización de las actividades básicas diarias.- Carecer de vivienda propia o ser ésta inadecuada y/o encontrarse en riesgo por vivir solo.- Estar dispuesto a ingresar en un Centro Residencial Asistido cuando no se valga por sí mismo.1http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=ce926545f951c010VgnVCM2000000c205a0aRCRDJOSE MARTINEZ DE VELASCOCALLEV22.0NaNNaNNaNNaNMADRIDMADRID280073.0ESTRELLA3.0RETIRO443346447399240.414615-3.667775915 571 319NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
111824Apartamentos Municipales para Mayores 'San Francisco'NaNNaNAlojamiento.Metro: LatinaBus: 3, 148Condiciones de Admisión: - Estar empadronado en el municipio de Madrid.- Ser mayor de 65 años (también el cónyuge que conviva con él, siempre que tenga cumplidos los 60 años).- Ser autónomo en la realización de las actividades básicas diarias.- Carecer de vivienda propia o ser ésta inadecuada y/o encontrarse en riesgo por vivir solo.- Estar dispuesto a ingresar en un Centro Residencial Asistido cuando no se valga por sí mismo.1http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=8300d4985261c010VgnVCM1000000b205a0aRCRDJERTECALLEV3.0NaNNaNNaNNaNMADRIDMADRID280051.0PALACIO1.0CENTRO439289447359740.410771-3.715552913 644 005NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
211545193Centro de día Gerontológico de Estancia DiurnaNaNNaNNaNMetro: San Francisco (línea 11). Bus: 118, 47.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=e3a5eabb8aa7d710VgnVCM1000001d4a900aRCRDBELZUNEGUICALLEV15.0NaNNaNNaNNaNMADRIDMADRID280255.0PUERTA BONITA11.0CARABANCHEL437271446974140.375893-3.738943915 254 211NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
311542466Centro de día Vivendia Personas MayoresNaNNaNNaNMetro: Usera (línea 6). Bus: 78.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=e95685f23df6d710VgnVCM2000001f4a900aRCRDLUIS USERACALLEV4.0NaNNaNNaNNaNMADRIDMADRID280267.0PRADOLONGO12.0USERA440051447019140.380158-3.706231914 766 831NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
411545182Centro de día para enfermos de alzheimer Reina Sofía (calle Infanta Mercedes 26)NaNNaNNaNMetro: Tetuán (línea 1). Bus: 126, 3. Bicimad: Estaciones 155 (calle San Germán, 57), 154 (calle Orense, 36), 152 (avenida del General Perón, 4). Aparcamiento: Presidente Carmona (33), avenida Presidente Carmona.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=83c3eabb8aa7d710VgnVCM1000001d4a900aRCRDINFANTA MERCEDESCALLEV26.0NaNNaNNaNNaNMADRIDMADRID280202.0CUATRO CAMINOS6.0TETUAN440787447855640.455565-3.698340913 308 842NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
511545229Centro de día para mayores Cruz Roja Española edificio Muguet 7NaNNaNNaNMetro: Carabanchel Alto (línea 11). Bus: 108, 118.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=773c3fc00ea7d710VgnVCM1000001d4a900aRCRDMUGUETCALLEV7.0NaNNaNNaNNaNMADRIDMADRID280446.0BUENAVISTA11.0CARABANCHEL436618446906840.369783-3.746559915 325 555NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
611542485Centro de día para mayores Doctor EspinaNaNNaNNaNMetro: Oporto (líneas 5 y 6). Bus: 34, 35, 118.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=d129df004107d710VgnVCM2000001f4a900aRCRDDOCTOR ESPINACALLEV16.0NaNNaNNaNNaNMADRIDMADRID280192.0OPAÑEL11.0CARABANCHEL438223447126640.389708-3.727869914 724 539NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
711542516Centro de día para mayores Domusvi ChamartínNaNNaNNaNMetro: Alfonso XIII (línea 4). Bus: 73, 9, 72.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=e100df004107d710VgnVCM2000001f4a900aRCRDANTONIO SALCESCALLEV1.0NaNNaNNaNNaNMADRIDMADRID280022.0PROSPERIDAD5.0CHAMARTIN443130447741540.445446-3.670614917 440 310NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
835679Centro de día para mayores El BosqueNaNNaNNaNMetro: O'Donnell (línea 6). Bus: C2, 143, 156, 2, 56, 71, 12, 30, C1. Bicimad: Estación 184 (paseo Marqués de Zafra, 24).NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=efcb444a2951c010VgnVCM2000000c205a0aRCRDLOS PEÑASCALESCALLEV14.0NaNNaNNaNNaNMADRIDMADRID280283.0FUENTE DEL BERRO4.0SALAMANCA443499447502440.423938-3.666044915 732 828NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
917464Centro de día para mayores GeriacenNaNNaNNaNMetro: Begoña (línea 10). Bus: 124, 137, 135, 175, 178. Cercanías Renfe: Ramón y Cajal (líneas C3, C7, C8 y Regional).NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=150832df1d51c010VgnVCM2000000c205a0aRCRDVIRGEN DE ARANZAZUCALLEV1.0NaNNaNNaNNaNMADRIDMADRID280346.0VALVERDE8.0FUENCARRAL-EL PARDO441546448185640.485343-3.689693NaNNaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores

Last rows

PKNOMBREDESCRIPCION-ENTIDADHORARIOEQUIPAMIENTOTRANSPORTEDESCRIPCIONACCESIBILIDADCONTENT-URLNOMBRE-VIACLASE-VIALTIPO-NUMNUMPLANTAPUERTAESCALERASORIENTACIONLOCALIDADPROVINCIACODIGO-POSTALCOD-BARRIOBARRIOCOD-DISTRITODISTRITOCOORDENADA-XCOORDENADA-YLATITUDLONGITUDTELEFONOFAXEMAILTIPOUnnamed: 32
162175453Residencia para mayores y centro de día MontehermosoNaNNaNResidencia de personas mayores dependientes y centro de día.Metro: Alto de Extremadura (línea 6). Bus: 33, 36, 39, 65, 31.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=8627bff77961c010VgnVCM1000000b205a0aRCRDDOCTOR BLANCO NAJERACALLEV6.0NaNNaNNaNNaNMADRIDMADRID280113.0LUCERO10.0LATINA436935447342140.409010-3.743277915 267 585NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
1635937334Residencia para mayores y centro de día Nuestra Señora de MontserratNaNNaNResidencia de personas mayores mixta y centro de día.Metro: San Bernardo (líneas 2 y 4). Bus: 147, 3, 21, C03. Bicimad: Estación 12 (calle San Bernardo, 85).NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=cfdbe25ed61ed210VgnVCM1000000b205a0aRCRDSAN BERNARDOCALLEV79.0NaNNaNNaNNaNMADRIDMADRID280155.0UNIVERSIDAD1.0CENTRO440071447546340.427646-3.706512914 471 250NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
16496739Residencia para mayores y centro de día PeñuelasNaNNaNResidencia de personas mayores dependientes y centro de día.Metro: Acacias (línea 5), Pirámides (línea 5). Bus: 62. Cercanías Renfe: Pirámides (líneas C1, C10 y Regional).NaN1http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=72fe4f36ff81c010VgnVCM1000000b205a0aRCRDARGANDACALLEV11.0NaNNaNNaNNaNMADRIDMADRID280052.0ACACIAS2.0ARGANZUELA440040447253840.401285-3.706590915 060 596NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
165131562Residencia para mayores y centro de día Plata y CastañarNaNResidencia de personas mayores dependientes y centro de día.NaNBus: 76.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=10c015a11a81c010VgnVCM1000000b205a0aRCRDPLATA Y CASTAÑARPASEOV38.0NaNNaNNaNNaNMADRIDMADRID28021NaNSAN ANDRESNaNVILLAVERDE438949446710340.352254-3.718925917 987 912NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
16611367Residencia para mayores y centro de día VallecasNaNNaNResidencia de personas mayores autónomas y centro de día.Bus: 54.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=c94914c4d661c010VgnVCM1000000b205a0aRCRDBENJAMÍN PALENCIACALLEV25.0NaNNaNNaNNaNMADRIDMADRID280385.0PORTAZGO13.0PUENTE DE VALLECAS445026447176140.394638-3.647770917 791 440NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
167112273Residencia para mayores y centro de día Villaverde AlzheimerNaNNaNResidencia de personas mayores dependientes y centro de día.Bus: 123, 130, 23. Metro: Villaverde Bajo - Cruce (línea 3). Renfe: Villaverde Bajo (líneas C3, C4 y Regional).NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=e1d92189cb81c010VgnVCM1000000b205a0aRCRDDE VILLAVERDE A VALLECASCARRETERAV38.0NaNNaNNaNNaNMADRIDMADRID280214.0LOS ROSALES17.0VILLAVERDE441732446752940.356281-3.686194917 230 961 / 917 230 962 / 917 230 963NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
1686725657Residencia para personas mayores MoratalazNaNNaNResidencia de personas mayores dependientes.Metro: Pavones (línea 9). Bus: 30, 71, 8.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=45a3d83d582bb310VgnVCM1000000b205a0aRCRDLUIS DE HOYOS SAINZCALLEV196.0NaNNaNNaNcon vuelta a calle TemploMADRIDMADRID280301.0PAVONES14.0MORATALAZ446594447238740.400380-3.629347917 730 483 / 917 730 495NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
1695937351Residencia sacerdotal para mayores San PedroNaNNaNResidencia de personas mayores mixta.Metro: San Bernardo (líneas 2 y 4). Bus: 147, 3, 149, 37, 21, C03. Bicimad: Estación 3 (Plaza Conde Suchil, 3).NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=3d82e25ed61ed210VgnVCM1000000b205a0aRCRDSAN BERNARDOCALLEV101.0NaNNaNNaNNaNMADRIDMADRID280152.0ARAPILES7.0CHAMBERI440155447582140.430877-3.705555914 454 200NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
17010846537Residencia y Centro de día para mayores Albertia MoratalazNaNNaNCentro de día y residencia de personas mayores mixta.Metro: Pavones (línea 9). Bus: 30, 71, 8, 32, 20.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=140b6ef4717a7610VgnVCM2000001f4a900aRCRDHACIENDA DE PAVONESCALLEV261.0NaNNaNNaNNaNMADRIDMADRID280301.0PAVONES14.0MORATALAZ446485447230040.399588-3.630615913 246 800NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores
1715780684Residencia, centro de día y pisos tutelados para mayores UseraNaNNaNResidencia de personas mayores dependientes, pisos tutelados y centro de día.Bus: 6, 60, 81, 78.NaN3http://www.madrid.es/sites/v/index.jsp?vgnextchannel=bfa48ab43d6bb410VgnVCM100000171f5a0aRCRD&vgnextoid=3a0bc8a002b5a210VgnVCM2000000c205a0aRCRDCRISTO DE LA VICTORIACALLEV247.0NaNNaNNaNNaNMADRIDMADRID280267.0PRADOLONGO12.0USERA439719447004240.378777-3.710148913 307 200NaNNaN/contenido/entidadesYorganismos/CentrosAtencionSocialMayores/ResidenciasApartamentosMayores